Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2002.09963
Cited By

Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias
and Variance

Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance

ACM Transactions on Knowledge Discovery from Data (TKDD), 2020

23 February 2020

Matthew Almeida

ArXiv (abs)PDF HTML

Papers citing "Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance"

5 / 5 papers shown

Enhancing Fairness and Performance in Machine Learning Models: A
Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality

Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality

231

5

0

12 Apr 2024

DNA: Denoised Neighborhood Aggregation for Fine-grained Category
Discovery

DNA: Denoised Neighborhood Aggregation for Fine-grained Category DiscoveryConference on Empirical Methods in Natural Language Processing (EMNLP), 2023

381

5

0

16 Oct 2023

Boosting Fair Classifier Generalization through Adaptive Priority
Reweighing

Boosting Fair Classifier Generalization through Adaptive Priority ReweighingACM Transactions on Knowledge Discovery from Data (TKDD), 2023

412

2

0

15 Sep 2023

Exploring the Learning Difficulty of Data Theory and Measure

Exploring the Learning Difficulty of Data Theory and MeasureACM Transactions on Knowledge Discovery from Data (TKDD), 2022

381

10

0

16 May 2022

Learning across label confidence distributions using Filtered Transfer
Learning

Learning across label confidence distributions using Filtered Transfer LearningInternational Conference on Machine Learning and Applications (ICMLA), 2020

Andrew E. Brereton

97

3

0

03 Jun 2020

Page 1 of 1